Parties to litigation typically have to share relevant evidence with opposing counsel through the discovery process. Attorneys typically “meet and confer” to establish criteria for what must be produced, and then each party makes a reasonable search of their records based on these criteria, providing the results to the opposite party. Discovery typically involves the gathering of potentially relevant materials, much of it digital, and then reviewing such materials to determine what to be shared with opposite parties.
A majority of documents are presently created and maintained electronically. The production and storage of electronic documents at high volumes and diversity of such documents produces new challenges regarding preservation, review, and admissibility of evidence. During discovery the electronic data is located, searched, and reviewed for their use in legal proceedings. Discovery involves the selection of appropriate search and information retrieval techniques having high levels of quality assurance and sound rationale regarding why such techniques were employed. Discovery of electronic documents is often a complex and expensive task including the engagement of different actors for the preservation, collection, data processing, and review. Moreover, the timing of discovery is often governed by scheduling orders, resulting in short periods of time designated to review electronic documents.
Therefore, a need exists for machine learning methods and apparatus to prevent expensive and overly time-consuming discovery disputes while maintaining accuracy and high quality standards in the production of evidence.